DuctApe: a suite for the analysis and correlation of genomic and OmnilogTM Phenotype Microarray data

Marco Galardini; Alessio Mengoni; Emanuele G. Biondi; Roberto Semeraro; Alessandro Florio; Marco Bazzicalupo; Anna Benedetti; Stefano Mocali

arXiv · 1307.4276

DuctApe: a suite for the analysis and correlation of genomic and OmnilogTM Phenotype Microarray data

Marco Galardini·Alessio Mengoni·Emanuele G. Biondi·Roberto Semeraro·Alessandro Florio·Marco Bazzicalupo·Anna Benedetti·Stefano Mocali

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Abstract

Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLogTM Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on genephenotype correlation are still missing. Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented. The source code and tutorials are available at http://combogenomics.github.io/DuctApe/.

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Marco Galardini, Alessio Mengoni, Emanuele G. Biondi, Roberto Semeraro, Alessandro Florio, Marco Bazzicalupo, Anna Benedetti, Stefano Mocali. 2013-12-13. DuctApe: a suite for the analysis and correlation of genomic and OmnilogTM Phenotype Microarray data. https://arxiv.org/abs/1307.4276

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